“Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“A full-throated defense of economic dynamism.” - The Wall Street Journal
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
“Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
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The AI Price War Is Here, Piling Pressure on OpenAI and Anthropic

Bradley Olson and Tina Li Wall Street Journal

Date Posted:: June 12, 2026

Token pricing for Anthropic’s new Fable 5 model is 50x that of DeepSeek’s V4 Pro. A Citadel notes cautions, “Even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints and marginal returns.”

Open-source models cost far less per token, the basic unit of AI computing. Anthropic’s recently-released Fable 5 model is more than 50 times more expensive per token than DeepSeek’s V4 Pro. The top proprietary models from companies like OpenAI, Anthropic or Google remain four to six months ahead of open-source competitors, researchers say. In some cases that means they can complete a complex task using fewer tokens, equating to a lower total cost. The shift to cheaper models appears to have played a role in a recent decline in a widely followed index that tracks AI spending, Citadel Securities said in a report this week. “Even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints and marginal returns,” the report said.

Takeaways by Macro Roundup^® AI

Anthropic’s Claude 3.5 costs 50x more per token than DeepSeek V4 Pro, driving enterprises toward cheaper open-source alternatives and depressing.
Proprietary models maintain 4–6 month capability leads over open-source competitors, enabling lower total costs despite higher per-token pricing and sustaining.

The College Wage Premium in the Generative AI Era

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network

Date Posted:: September 4, 2026
Is Database:: Database

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

Takeaways by Macro Roundup^® AI

The U.S. college wage premium fell from 0.626 in 2022 to 0.575 in 2026—the first sustained negative relative demand growth for college labor in four decades, per Current Population Survey data.
Moving from zero to full occupational AI exposure reduced wages by 8.6 percentage points by 2026; because college graduates concentrate in high-exposure white-collar roles, this mechanism accounts for roughly 28% of the premium’s compression.

The AI Re-Acceleration That Wasn’t

Paul Kedrosky Applied Complexity

Date Posted:: September 3, 2026
Is Database:: Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup^® AI

Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

The AI Trade Is Losing One of Its Key Signals

Jan-Patrick Barnert and Michael Msika Bloomberg

Date Posted:: July 7, 2026
Is Database:: Database

The Silicon Data LLM Token Expenditure Index, which tracks AI token prices, is down ~20% from its recent peak in May. The metric is a rough “proxy for marginal willingness to pay.”

A softer index doesn’t mean AI is getting cheaper. The gauge blends prices and usage, meaning a dip can imply very different scenarios: either list prices are falling, or demand is shifting toward cheaper models. It could also point to a genuine softening in what buyers are prepared to shoulder. Each of these possibilities carries different implications. Let’s start with a benign read: While token prices have collapsed more than 90% since 2023, total spend has roughly doubled since last year. Cheaper tokens have expanded the market. This means that an index pause is simply digestion, while demand is real and capex is money well spent. The bull case for Nvidia rests here. Now for the interpretation that’s keeping people up at night: Bears warn that sustained weakness in the index could end the trade that saw nearly the entire AI cohort rally hard this cycle. It’s token spending that justifies the next capex order, and the bill is already looking stretched. Allianz Research said there’s nearly a 46% growth gap between AI investment and sales. That’s worse than the 32% divergence measured during the 2001 telecom bust.

Takeaways by Macro Roundup^® AI

Token prices collapsed 90% since 2023 while total spending doubled, driving market expansion despite index softening.
Index weakness signals either benign demand shift toward cheaper models or stretched capex justification, leading to divergent bull/bear interpretations.

The Age Of The Solopreneur

Ernie Tedeschi, Marisa Rama and Chris Cruickshank Stripe Economics

Date Posted:: June 22, 2026
Is Database:: Database

New US business applications have surged since late 2024, driven mainly by “likely nonemployer” filings representing “solopreneurs” rather than businesses expected to hire. Data suggest AI may be enabling this by filling capability gaps without hiring.

While the recent surge in US business formation is not reflected in high-propensity applications, the evidence points toward a structural increase in genuine small business activity over 2025 and 2026, driven by solopreneurs. Preliminary evidence of growth in AI tool usage and solopreneur growth in high-AI-adoption sectors suggests that advances in AI are responsible for a meaningful portion of this growth. We believe AI is lowering barriers to business formation and growth by expanding the capabilities of solopreneurs, further improving tools and platforms that cater to new businesses, and creating a new set of opportunities for entrepreneurs to pursue. Whether the magnitude of this effect is as large as the most optimistic readings of the data suggest remains to be seen, but we believe we might be in the early innings of a fundamental acceleration in business formation—which could have ripple effects throughout the economy.

Chart of the Day: Small Models are Closing the Gap to Frontier AI

Paul Kedrosky Applied Complexity

Date Posted:: June 3, 2026
Is Database:: Database

Small models have increasingly closed the performance gap with frontier models. To Kedrosky, this implies “edge compute becomes real,” which in turn, at the margin, means reduced relative demand for data center-based versus local compute.

A quiet story in AI isn't that large models keep getting better, it's that small ones are catching up quickly. In 2022, reaching 60% on MMLU, a test of academic reasoning, required a 540-billion-parameter model. By 2024, a 3.8-billion-parameter model hit the same threshold — a 142-fold parameter reduction in two years. The direction is clear, and the implications are wide-ranging. Edge compute becomes real. Models that fit on a phone or a sensor don't need a data center. Inference shifts from hyperscaler to device, which is a different supply chain and a different competitive moat than anyone has built for. The data center API business model has a half-life. If a fine-tuned 7B model running locally can handle 80% of enterprise use cases, the case for paying per-token to a cloud provider shrinks fast. The most exposed providers are those whose moats are model quality rather than distribution, i.e., the capex-heavy frontier models. Compute becomes less scarce.

Takeaways by Macro Roundup^® AI

Inference migration from data centers to devices results in a fundamentally different supply chain, threatening capex-heavy frontier model providers lacking.
By 2024, a 3.8-billion-parameter model hit the same threshold — a 142-fold parameter reduction in two years.
The direction is clear, and the implications are wide-ranging.

Challenging the Narrative of European Decline, Continued

Paul Krugman Paul Krugman Wonks Out

Date Posted:: May 19, 2026
Is Database:: Database

In 2021 prices, per capita GDP in the Euro area is falling relative to that of the US, but the opposite is true if one takes into account the benefits to Europe from falling tech prices. “The big benefits of IT come from applying it, [not] creating it.”

Let’s start by looking at GDP per capita in Europe (actually the euro area) as a percentage of GDP per capita in the US. If we do this using constant prices — the World Bank uses 2021 prices — we get the line in Chart 1 labeled “2021 prices.” This line shows Europe falling behind over the past 25 years. If, however, we simply use prices in each given year, we get the line labeled “PPP,” which shows Europe gaining on the US. We get a similar picture if we look at GDP per worker-hour, where the black line is calculated using 2021 prices and the blue line is calculated using PPP. IT progress is passed on to all consumers via lower prices. The big benefits of IT come from applying it, rather than creating it. And as I’ve tried to show, the data show Europe holding its own in the relative value of the goods it produces, indicating that European economies are doing fine when it comes to applying technological advances. What should worry Europe, instead, are the geopolitical implications of US/Chinese leadership in advanced technology. The risk of being cut off from strategically important technologies, once minimal, is now very real. And that risk, rather than misleading numbers about trends in real GDP per worker hour, is what should concern European policymakers.

Takeaways by Macro Roundup^® AI

If we do this using constant prices — the World Bank uses 2021 prices — we get the line in.
If, however, we simply use prices in each given year, we get the line labeled “PPP,” which shows Europe gaining.
We get a similar picture if we look at GDP per worker-hour, where the black line is calculated using 2021.

Token pricing for Anthropic’s new Fable 5 model is 50x that of DeepSeek’s V4 Pro. A Citadel notes cautions, “Even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints and marginal returns.”

Are proprietary AI models losing their pricing power to cheaper alternatives?

Takeaways by Macro Roundup® AI

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The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is the college degree losing its economic value to artificial intelligence?

Takeaways by Macro Roundup® AI

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Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Does AI capability growth actually accelerate or just appear to?

Takeaways by Macro Roundup® AI

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The Silicon Data LLM Token Expenditure Index, which tracks AI token prices, is down ~20% from its recent peak in May. The metric is a rough “proxy for marginal willingness to pay.”

Is artificial intelligence investment growing faster than actual demand?

Takeaways by Macro Roundup® AI

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New US business applications have surged since late 2024, driven mainly by “likely nonemployer” filings representing “solopreneurs” rather than businesses expected to hire. Data suggest AI may be enabling this by filling capability gaps without hiring.

Are AI tools fundamentally reshaping the economics of starting a business?

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Small models have increasingly closed the performance gap with frontier models. To Kedrosky, this implies “edge compute becomes real,” which in turn, at the margin, means reduced relative demand for data center-based versus local compute.

Are smaller AI models making cloud computing less essential for artificial intelligence?

Takeaways by Macro Roundup® AI

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In 2021 prices, per capita GDP in the Euro area is falling relative to that of the US, but the opposite is true if one takes into account the benefits to Europe from falling tech prices. “The big benefits of IT come from applying it, [not] creating it.”

Does measuring European economic output differently change the decline narrative?

Takeaways by Macro Roundup® AI

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Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

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Takeaways by Macro Roundup^® AI